Product Requirements Document (PRD) template for Lovable Project

On this page from MakeMyPRD, you'll get a real Product Requirements Document (PRD) template made for a Lovable project, including a filled-out example with metrics. See exactly what a high-quality PRD looks like before building your next lovable, user-focused product. Then, generate your own customized PRD instantly.

What this is

A Product Requirements Document (PRD) template for Lovable Project defines the “what” and “why” of a product in granular, actionable terms. For PMs, engineers, and founders, a PRD organizes essential requirements, acceptance criteria, dependencies, and success metrics in a repeatable format. Modern teams often maintain PRDs in tools like Bolt, Notion, or directly in repos via Markdown with Cursor. This structure makes it easier to align on priorities, reduce misunderstandings, and keep Lovable-powered projects on track, especially as teams scale or iterate with frameworks such as Next.js or Supabase. The best PRDs leave no ambiguity about user problems, technical constraints, and how success is measured.

Compared to alternatives

Option	Best for	Trade-off
MakeMyPRD	Generating fast, opinionated PRDs with Lovable, AI, and customizable metrics.	Less tailoring for highly unconventional teams; structure is opinionated.
Notion + Handcrafted PRD	Teams with highly bespoke workflow, needing deep integration and collaboration.	Manual, slower. Easy to go off-standard, harder for onboarding new PMs.
Confluence PRD Template	Organizations already committed to Jira/Atlassian ecosystem.	Rigid, lots of boilerplate, can get bloated over time.
Google Docs PRDs	Quick, collaborative drafting with comments and version history.	No enforced structure, prone to drift and incomplete specs.
Bolt Templates	Engineering-heavy orgs building modular documentation architectures.	Requires tool adoption; may be overkill for early founders.
Cursor markdown in repo	Engineering teams who want PRDs close to the codebase.	Harder for non-technical contributors to engage.

A real example

Filled example

A real, ready-to-customize version

Product Requirements Document – Lovable Project: PlantPal

Product Overview PlantPal is a Lovable-powered mobile companion that helps apartment dwellers keep their houseplants happy, healthy, and alive—with zero prior botany knowledge. Target users: urban millennial renters with 1-6 plants; US, Canada, UK.
Problem Statement Over 40% of new plant owners abandon care routines after 3 weeks. Users say, “I always intend to water them, but then forget or don't know the right schedule.”
Solution Statement A personalized app that:

Scans each plant via camera (using Lovable's Vision API + Supabase for fast onboarding)
Sets watering and care reminders, tailored to species
Provides real-time emergency plant triage (e.g., drooping, yellowing detection)

Goals & Success Metrics

70% DAU/MAU after 2 months
Reduce plant loss reports by 50% vs. baseline
<2 min to onboarding (scan & set up first 3 plants)
NPS of 60+ for users in cities >1M residents

Core Requirements

Lovable integration for plant species ID (iterate on accuracy; baseline 85%)
Reminders via push (via Vercel backend)
Emergency support (template-based) using Claude Code; answers within 10s
Activity dashboard (built in Next.js)

Non-goals

No physical devices or hardware integrations in v1
No marketplace features

Dependencies

Lovable AI Vision API
Supabase DB setup
Stripe for premium in-app features

Open Questions

How to localize care guides for non-English speakers by Q3?
Should we support plant trading or just care?

Milestones

Alpha: week 4; Closed beta: week 8; Public launch: week 12.

How to use this

Identify a user problem worth loving: Interview or survey at least 10 target users; catalog their real frustrations. Quantify the issue—don't settle for vague pain points.
Document concrete success metrics: Set measurable goals: DAU/MAU, setup time, bug rate, or NPS. Pick at least two with actual numbers attached.
Specify core requirements and features: List features that directly solve user problems. Back each with at least one tool—e.g., Lovable for core UX, Vercel for infra.
Note what you won't build (non-goals): State clearly which features or integrations are intentionally excluded from v1. This keeps scope tight.
Capture dependencies and blockers: Link every major requirement to a dependency, such as APIs, frameworks, or integrations. Identify anything that could block launch.
Align on user experience with example flows: Draft a fast walkthrough—how does a user interact, step by step? Use real tool names (e.g., scan with Lovable, dashboard with Next.js).

FAQ

What's the minimum content for a Lovable project PRD?

At a minimum, your PRD should have a concise problem statement, clear success metrics (like NPS or DAU/MAU), a core solution outline, and a list of must-have requirements. Including both what you will and won't build (non-goals), dependencies (Lovable, Cursor, Supabase, etc.), and at least one measurable milestone increases clarity and alignment.

How do I make my PRD actionable for engineers?

Translate requirements into user stories or acceptance criteria. Link each requirement to the actual tool or API the engineering team will use (such as Lovable Vision API, Supabase backend, or Vercel edge functions). Attach target metrics and concrete technical constraints to avoid ambiguity and reduce back-and-forth.

Can I adapt the PRD template for an AI-first product?

Absolutely. Lovable, Claude Code, Cursor, and Next.js all fit naturally in this structure. Call out model dependencies or training data sources inside 'Dependencies.' Define AI-specific success metrics (e.g., query latency, accuracy, AI-powered onboarding time) just as you would user engagement metrics.

Where should I store my PRD for a fast-moving, remote team?

Store it where your team already works: Bolt or Notion for structured docs, Cursor for in-repo Markdown, or MakeMyPRD for rapid generation. Teams integrating with APIs (like Supabase, Vercel) often prefer their PRDs right alongside code and data models.